Enterprise Data Governance

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Enterprise Data Governance

Aligning Data Quality Management

With Your Data Governance Program

Presented by:

Mark Allen

Sr. Consultant, Enterprise Data Governance

WellPoint, Inc.

([email protected])

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Introduction:

Mark Allen is a senior consultant and enterprise data governance lead at WellPoint, Inc. Prior to WellPoint, Mark was a senior program manager in customer operation groups at both Sun

Microsystems and Oracle Corporation. At Sun Microsystems, Mark served as the lead data steward for the customer data domain throughout the planning and implementation of Sun’s

customer data hub. Mark has more than 20 years of data management and project management experience including extensive planning and deployment experience with customer master

initiatives, data governance, data integration projects, and leading data quality management practices. Mark has served on various customer advisory boards focused on sharing and enhancing MDM and data governance practices. Mark is also co-author of the book:

Master Data Management in Practice: Achieving True Customer MDM (John Wiley & Sons, 2011).

Contact [email protected] visit http://www.mdm-in-practice.com

WellPoint, Inc:

WellPoint is one of the largest health benefits companies in the United States:

• Revenue: $61.7 billion (2012),

• Net Income: $2.6 billion (2012)

• Employees: 43,650

• Nearly 36 million members in its affiliated health plans

• Nearly 68 million individuals served through its subsidiaries.

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Presentation Topics

• Aligning Data Governance with IT

Governance and Project Governance

• Building Data Quality Management within

the Data Governance Program

• Creating and Maintaining an Enterprise

Footprint

• Governing the Data Analysis and Quality

Improvement Process

Governance

Data Quality Management

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• Aligning Data Governance with IT

Governance and Project Governance

• Building Data Quality Management within

the Data Governance Program

• Creating and Maintaining an Enterprise

Footprint

• Governing the Data Analysis and Quality

Improvement Process

Data Governance

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Stuck in Neutral

Companies can struggle with achieving their data management

and quality management objectives because of fundamental

organization and process alignment issues, such as:

 Not having data governance aligned well with IT governance

and project governance programs

 Organizational changes that can slow momentum or

fragment governance and quality management focus

 Not working collectively to correct root causes of data issues

and randomly applying point fixes

 Different groups having different systems of record or using

different reporting and analytic solutions

Governance

From the TDWI Best Practice Report ’Next Generation Master Data Management’ (2012), among the top responses from users surveyed regarding their challenges to MDM success were:

- Lack of cross-functional cooperation

- Coordination with other disciplines (DI, DQ)

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We have hired a consulting firm to do a master data management

assessment. As project manager I need to quickly pull together a current view of our data architecture and data flows but I am finding it hard to get our data architects to commit enough time for this. I have expensive consultants ready and waiting.

I spent weeks creating a quality dashboard for our product marketing group.

When I previewed this with our Sales and Finance teams they disagreed with a number of my calculations and results. They pointed me to other data and metrics but I can’t get clear answers about that data and those calculations.

You would think that there should be just one common version of account codes or country codes, but instead each of our source systems seems to have its own versions causing me a lot of extra time each month to normalize and recheck this data for executive reports I deliver.

I work in our Customer Service. We are continually challenged because our customer data is inconsistent and not centralized. This clearly

impacts the quality of service we deliver to our customers but we can’t seem to get connected with our other groups who can help address this^.

Alignment Issues and Frustrations

(images and captions for illustrative purposes only) 6

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Lack of Alignment

• Relationships and handshakes between the various governance functions are not well established.

• A Data Governance program exists but lacks sufficient authority and reach.

• IT Governance is not transparent and occurs through various boards and architecture review committees.

• Project Governance functions are distributed across business units resulting in planning inefficiencies and overlaps.

• There is no common data quality management strategy and

framework.

• Business terms, metrics, data models, and metadata are inconsistent.

Data Quality Management

?

IT

Governance

Project Governance Data

Governance

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Companies are trying to address the alignment

issues with more integrated data governance

and quality management strategies

From the Information Difference Research Study ’How Data Governance links Master Data Management and Data Quality’ (Aug 2010) involving 257 world-wide companies:

• 58% indicated that their plans for implementing data governance will be part of a broad data management initiative involving data quality.

• “Better quality and faster decisions making” was the top response when asked what are the main benefits they expect Data Governance to deliver.

From the Kalido White Paper ’The Role of Data Quality Monitoring in Data Governance’ (Feb 2011) prepared by Jim Harris, Obsessive-Compulsive Data Quality:

“Data governance provides the framework for a proactive approach to data quality, which requires going beyond reactive data cleansing projects, and establishing a pervasive program for ensuring that data is of sufficient quality to meet the current and evolving business needs of the organization.”

From the TDWI Best Practice Report ’Next Generation Master Data Management’ (2012), the top reasons for implementing MDM were:

1. Complete views of business entities 2. Sharing data across the enterprise 3. Data-based decisions and analyses 4. Customer Intelligence

5. Operational excellence

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• Use Data Governance as the aligning function with the

IT and Project areas.

• Work with IT management and the project planning

teams to define clear charters, roles, and engagement

opportunities.

• Leverage cross-functional collaboration where it already

exists in projects and programs.

• Bring attention to where good governance and quality

management practices are occurring. Build from best

practices.

• Be persistent, be opportunistic, but have patience.

Key to Building Alignment and Collaboration

Governance

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An Aligned State

[email protected] June 2012, DGIQ Conference 10

• Governance functions have formal relationships and clear charters.

• Actions and decisions are coordinated through

common strategies, processes, and forums.

• Data stewardship is a core competency with support from IT and Project

resources.

• Data Quality Management is driven through Data

Governance but supported by IT and Project

governance functions.

• Data Quality requirements are defined and applied to IT and Business projects.

IT

Governance Project Governance Data

Governance

Charter:

• Coordination of Enterprise Data Governance Practices

• Data Quality Management Polices and Standards

• Defines Data Steward Roles &

Responsibilities

• Controls Enterprise Business Terms and Rules

• Engaged in IT and Project Governance Decisions

Charter:

• IT Strategies, Investments, Tools, Infrastructure

• Information Architecture, System Architecture

• Technical Review and Solutions.

• Technical Support

• Metadata Management Support

• Engaged in Data Governance and Project Governance Decisions

Charter:

• Project Review, Plan, Budgets

• Project Management, Strategies, Roadmap

• Resource Allocation

• Project Testing, Delivery, and Support

• Support of Data Quality Requirements

• Engaged in IT and Data Governance Decisions Data Quality

Management

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• Aligning Data Governance with IT

Governance and Project Governance

• Building Data Quality Management within

the Data Governance Program

• Creating and Maintaining an Enterprise

Footprint

• Governing the Data Analysis and Quality

Improvement Process

Governance

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Data Quality Management Needs To Be…

• A key discipline and function of data governance

• Expressed with clear, measureable milestones in a

data governance maturity model

• Supported through data governance and quality

requirements in the solution design process

• Supported by data stewards and IT associate who

are members of the governance team

• Supported by consistent business processes and IT

solutions

Building Data Quality Management within

the Data Governance Program

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DQM As A Data Governance Function

Our Data Governance program has been established to define an enterprise-wide data governance foundation and on-going program strategy.

Governance Adoption

& Maturity Metrics

Governance Intake and Decision Metrics

Data Quality Metrics

Metadata Management Metrics

Guiding Principles Data Governance

Model

Data Governance

Measurement

Our data is a strategic enterprise asset with people accountable for its management, quality and integrity.

Our Data Governance policies, standards, and quality requirements will be key factors in our Program Management Office (PMO) and

Solution Design (SDLC) processes.

Data Governance Charter

Processes Services Controls Metrics Data

Stewardship

Metadata Management

Quality Management

Functional Model

Governance

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DQM In A Governance Maturity Model

Level 1 Marginal & Reactive:

Data governance is at best a marginal and non-formalized

practice. There is need for a more formal structure.

Level 2 Defined & Initiated Data governance has been defined and implemented

within an EDG domain structure, process, and

context.

Level 3

Sustainable & Proactive Data governance is an ongoing practice with active processes engaged in the data

and project life cycles for the data domain.

Level 4:

Optimized & Integrated Data governance is a core competency throughout the

enterprise providing a key role in the EIM and MDM

strategies.

Data Steward Leads and sub-teams are in place

Consistent use and control of business terms and reference data

Data models and

dictionaries are maintained and updated

Data life cycle flows exists

Data source to target mapping is well defined

Key data entities and elements have been identified

Data quality is measured and actively reviewed

Data quality improvement initiatives are ongoing

Data governance and quality management policies are cataloged

Governance and quality requirements are part of SDLC process

A formal data governance charter exists

Data ownership and steward roles are defined

DG processes and collaboration sites have been implemented

DG communication and Training exists

Data model s exists for each data domain

Business Glossary and Metadata Repository solutions exists

Governance engagement exists in key projects

Governance activity metrics and maturity measurements exists

Data quality management orientation plans and processes are underway

Data quality analysis has been initiated

Data ownership is well established and data stewardship is a core competency

Data governance is well integrated with enterprise data architecture and information management strategies, plans, and investments

Master Data Management practices exists with data governance and quality management as key disciplines

Data quality improvement roadmaps are well described within an organization’s continual improvement plans

Risk is well managed with HR, Legal, Compliance, and Privacy Office actively engaged in the DG process

Data governance type decisions are occurring in an ad hoc manner in different decision areas

Data ownership is unclear and quality improvement efforts are disorganized.

Business terms lack standardizations and control

Code sets do not have consistent ownership and management

Some data models exist but are not complete or maintained

Enterprise data management strategies or projects are indicating need for data governance

Audit or compliance issues suggest need for formal data governance

Audits raise data issues and mitigation plans

Customer DG

Product DG

Finance DG

Sales DG

Marketing DG

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DQM Supported In A Solution Design Process

Discovery &

Requirements

Design &

Develop

Test &

Verify Implement Control &

Maintain Initiative

Planning

Solution Design Process

Engaged in data modeling,

data integration

plans, validation rules, and data policy decisions.

Involved in test and verification

efforts.

Governance team sign-off of data quality

and integrity.

Assist with readiness plans and facilitates resolution of

data related issues.

Involved in data quality

control, metrics, monitoring, and change management.

Data Governance

Identifies needs for data

governance involvement.

Creates data governance engagement

plans as needed.

Participation in discovery

and requirement

sessions.

Identifies any data impacts.

Responds to needs for data

analysis, standards,

general guidance, and

quality metrics.

Governance

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DQM Supported By Data Stewards & Analysts

Tactical

Focus

Operational

Focus

Metadata Management

Sub-Team

Sub-Team

Strategic

Focus

Compliance Management

Sub-Team

Data Governance Domain Team Structure

Data Domain Trustee

Data Governor Data Governor Data Governor

Data Steward

Data Steward Data

Architect

& Data Analyst Data

Governance

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Requirements from driver(s)

Analyze and review requirements

Evaluate compliance of requirements

Violation?

Submit request to DG for amendment

Amendment possible?

Requirements cannot be fulfilled

Request data analysis and profiling

Drive the design of solution(s)

Evaluate compliance of

solution(s)

Rules, policies &

procedures

Violation?

Rules, policies &

procedures

Submit request to DG for amendment

Amendment possible?

Requirements cannot be fulfilled Y

N

N Y

Y

N Y

N

DQM Supported By Processes and Solutions

Data Quality Dimension Completeness

Status Data Quality Index 12 Month Trend

Validity Consistency Duplication Accuracy

Detailed Reports

Data Definition Quality

Metadata Management

Sub-Team

Sub-Team

Sub-Team Data Domain

Trustee

Architect

& Data Analyst

Metadata Management

Sub-Team

Sub-Team

Trustee

Architect

Metadata Management

Sub-Team

Sub-Team

Trustee

Steward Data Steward Data

Architect

Governance

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• Aligning Data Governance with IT

Governance and Project Governance

• Building Data Quality Management within the

Data Governance Program

• Creating and Maintaining an Enterprise

Footprint

• Governing the Data Analysis and Quality

Improvement Process

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• Ensure there is a common enterprise-wide process for

data governance intake, item tracking, and decision

response.

• Ensure there is an Executive Council or Board of Trustees

forum for addressing enterprise data governance and

quality management strategies as well as other cross-

domain items or issues.

• Create a user friendly collaboration site and use common

enterprise platforms to support the processes and artifacts

of data governance and quality management

• Define a communication model and RACI matrix for

driving consistent communication

Creating and Maintaining an Enterprise

Footprint

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Governance Process Flow Example

Cross- Domain

issue?

Yes No

Qualified Request?

Engage Executive Council?

Executive Council Engagement Cross-

Domain Governance Engagement

Decision

Yes Yes No No

Data Governance

Intake Process &

Item Tracking Log

•Issues

•Data Quality

•Policies

•Standards

•Processes

•Support

•Compliance

•Metadata

•Monitoring

Domain Governance

Review

Data Governance Charter Processes Services Controls Metrics

Data Stewardship

Quality Management

Functional Model

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Cross-Domain Governance & Quality

Management

Cross-Domain Data Governance & Data Quality Council (Board of Trustees)

Customer Domain

Stewards Governors

Trustee

Program, Project, and Data Consumer Areas

DG PMO Leads

Product Domain

Stewards Governors

Trustee

Finance Domain

Stewards Governors

Trustee

Other Domains

Stewards Governors

Trustee

Analytics

Analytics Teams

Members

IT, Finance,

Legal, HR, Privacy

Other Key Members

Data Governance Intake Process, Issue Tracking, Meeting Facilitation

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Common Sites and Platforms Supporting

Data Governance & Quality Management

Collaboration Site

Governance Process

Enterprise Platforms

Enterprise Users

Cross- Domain issue?

Yes No

Qualified Request?

Engage Executive Council?

Executive Council Engagement Cross-

Domain Governance Engagement

Decision

Yes Yes

No No

Data Governance

Intake Process &

Item Tracking Log

•Issues

•Data Quality

•Policies

•Standards

•Processes

•Support

•Compliance

•Metadata

•Monitoring

Domain Governance

Review Data Governance Charter Processes Services Controls Metrics

Data Stewardship

Quality Management Functional Model

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Target Audiences (Who to communicate to)

How to Communicate (Communication

Channels)

What to Communicate

When to Communicate

Why to Communicate

• Executives

• Sponsors

• Trustees

• Stakeholders

• Data Governors

• Data Stewards

• Business Users

• Subject Matter Experts

• IT Governance

• IT Teams

• Project Governance

• Project Teams

• New Employees

• Newsletters

• SharePoint

• Wiki

• Bulletin boards

• Email

• Management communications

• Project meetings

• Departmental meetings

• Tailored messages for targeted audiences

• FAQ

• DG Charter and Objectives

• DG Structure and Teams

• Accomplishments

• Program News and Announcements

• DG Intake Process

• Other relayed processes

• Metrics and Dashboards

• Policies, Standards

• Meeting and decision info

• Training material

• FAQ

• Relevant Industry artifacts about DG

• Daily

• Weekly

• Bi-Weekly

• Monthly

• Quarterly

• Semi annually Annually

• As needed

• Gain trust

• Keep them informed

• Maintain a presence

• To invoke feedback

• Receive suggestions

• Value of DG and Data Management

Define a Communication Model and RACI

Matrix for Driving Consistent Communication

Data Governance Communication Model

RACI Matrix

Governance

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• Aligning Data Governance with IT

Governance and Project Governance

• Building Data Quality Management within

the Data Governance Program

• Creating and Maintaining an Enterprise

Footprint

• Governing the Data Analysis and Quality

Improvement Process

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• Focus on data analysis and quality improvement efforts

that have the most benefit to business operations and

analytics.

• Define, maintain, and publish enterprise standard data

validation rules, quality dimension definitions, and

scorecard formats. This will minimize variations in

quality measurement and format.

• Reduce overlapping solutions, resources, vendor tools,

and consulting engagements.

• Time and effort well spent with developing data

governance will translate over time into better data

quality management and less data quality issues.

Governing The Data Analysis and Quality

Improvement Process

Governance

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Focus on improvements that have the most

benefit to business operations and analytics

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Benefit

Cost

High

High Low

Low

Address Validation & Cleansing

Business Term Standardization

Customer Name Standardization

Account Code Cleanup Parts Code Standardization

Duplicate Customer Merge

Country Code Cleanup

Product Description Cleanup

Data Quality Training

Purchase Data Quality Analyzer Tool

Service Code Analysis

Duplicate Contact Cleanup

Parts Taxonomy Consolidation

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Data validation rules using standard quality

dimension definitions and scorecard formats

Item Item Title Requirement Description Measurement Criteria DQ Dimension

Zip Code Measure overall zip code field

completeness

Completeness

Measure zip code field completeness from each EPDS v2 data source

Completeness

Measure zip codes for valid format Validity Measure zip code field accuracy

within source systems. Accuracy should using the USPS database as the correct zip code reference source.

Accuracy

Each business entity has one and only one tax id.

Uniqueness

Each tax id has a valid format. Validity 3 Facility Type

Code

Need facility type code of each hospital. Each facility should have valid facility type.

Definitions should come from CMS (e.g., acute facility, SNF, VA hospital)

Each facility should have valid facility type. Definitions should come from CMS (e.g., acute facility, SNF, VA hospital)

Validity

4 D&B DUNS Reference

The D&B service provide DUNS numbers that represent unique Business Entity information to validate or augment a company's customer information. Need to use DUNS numbers as a cross-reference to ensure accuracy of customer data.

Percentage matched, percentage of variance, and percentage below match confidence level.

Accuracy Entry and format of zip codes are in source

systems is inconsistent causing zip code completeness and consistency issues in the enterprise data warehouse. More complete, consistent and accurate capture of zip codes data is required. This will have many benefits including creating more accurate location information for many business and customer services.

1

2 Tax ID Each legal business entity in US should have a unique tax id. Need to ensure these are unqiue and valid ids.

DQ Dimension Enterprise Definition

Completeness Completeness is the measure of missing data.

Consistency Consistency is the measure of the expected data values in one data set being equivalent with values in another data set.

Accuracy Accuracy is the measure of how correct the values agree with an identified reference source of information.

Referential Integrity

Referential Integrity is the measure of the condition that exists when all intended references from the data in one column of a table to data in another column of the same or different table is valid.

Uniqueness Uniqueness is the measure of when no entity exists more than once within the same data set.

Duplication Duplication is the measure of duplication existing within or across systems for a particular field, record, or data set.

Timeliness Timeliness is the measure of the degree to which data is available for use in the time frame in which it is expected.

Currency Currency is the measure of the degree to which information is current with the real world that it models. How

"fresh" the data is in relation to possible time related changes. Has been refreshed within a specified period of time.

Validity Validity is the measure of how well the data conforms to attributes associated with the data element such as its data type, precision, format patterns, range, or expected list of values for the field.

Accessibility Accessibility is the measure of being able to access data when it is required.

Credibility Credibility is the measure of the enterprise users trust and confidence in data.

Data Quality Dimension Completeness

Status

Data Quality Index 12 Month Trend

Validity Consistency Duplication Accuracy

Detailed Reports

Data Definition Quality Governance

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Benefit of aligned strategies and practices

Time

Effort

+

_ _

Data Governance

Data Quality Issues

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We have hired a consulting firm to do a master data management assessment. As project manager I need to quickly pull together a current view of our data architecture and data flows but I am finding it hard to get our data architects to commit enough time for this. I have expensive consultants ready and waiting.

I spent weeks creating a quality dashboard for our product marketing group.

When I previewed this with our Sales and Finance teams they disagreed with a number of my calculations and results. They pointed me to other data and metrics but I can’t get clear answers about that data and those calculations.

You would think that there should be just one common version of account codes or country codes, but instead each of our source systems seems to have their own versions causing me a lot of extra time each month to normalize and recheck this data for executive reports I deliver.

I work in our Customer Service. We are continually challenged because our customer data is inconsistent and not centralized. This clearly impacts the quality of service we deliver to our customers but we can’t seem get connected with our other groups who can help address this.